Efficient correction of inhomogeneous static magnetic field-induced distortion in Echo Planar Imaging

Analyses of regional radiosensitivity of white matter structures along tract axes using novel white matter segmentation and diffusion imaging biomarkers

BACKGROUND AND PURPOSE

Brain radiotherapy (RT) can cause white matter damage and downstream neurocognitive decline. We developed a computational neuroimaging tool to regionally partition individual white matter tracts, then analyze regional changes in diffusion metrics of white matter damage following brain RT.

MATERIALS AND METHODS

RT dose, diffusion metrics and white matter tract structures were extracted and mapped to a reference brain for 49 patients who received brain RT, and underwent diffusion tensor imaging pre- and 9-12 months post-RT. Based on their elongation, 23 of 48 white matter tracts were selected. The Tract-Crawler software was developed in MATLAB to create cross-sectional slice planes normal to a tract's computed medial axis. We then performed slice- and voxel-wise analysis of radiosensitivity, defined as percent change in mean diffusivity (MD) and fractional anisotropy (FA) as a function of dose relative to baseline.

RESULTS

Distinct patterns of FA/MD radiosensitivity were seen for specific tracts, including the corticospinal tract, medial lemniscus, and inferior cerebellar peduncle, in particular at terminal ends. These patterns persisted for corresponding tracts in left and right hemispheres. Local sensitivities were as high as 40%/Gy (e.g., voxel-wise: -39 ± 31%/Gy in right corticospinal tract FA, -45 ± 25%/Gy in right inferior cerebellar peduncle FA), p < 0.05.

CONCLUSIONS

Tract-Crawler, a novel tool to visualize and analyze cuts of white matter structures normal to medial axes, was used to demonstrate that particular white matter tracts exhibit significant regional variations in radiosensitivity based on diffusion biomarkers.

The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites

The ABCD study is recruiting and following the brain development and health of over 10,000 9–10 year olds through adolescence. The imaging component of the study was developed by the ABCD Data Analysis and Informatics Center (DAIC) and the ABCD Imaging Acquisition Workgroup. Imaging methods and assessments were selected, optimized and harmonized across all 21 sites to measure brain structure and function relevant to adolescent development and addiction. This article provides an overview of the imaging procedures of the ABCD study, the basis for their selection and preliminary quality assurance and results that provide evidence for the feasibility and age-appropriateness of procedures and generalizability of findings to the existent literature.

Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract-Specific Effects

BACKGROUND AND PURPOSE

Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients' white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data.

METHODS

Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts.

RESULTS

Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres.

CONCLUSIONS

Quantitative results of mean tract distortions on the order of 1-2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings.

Analysis strategies for high-resolution UHF-fMRI data

Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.

Working Memory And Brain Tissue Microstructure: White Matter Tract Integrity Based On Multi-Shell Diffusion MRI

Working memory is a complex cognitive process at the intersection of sensory processing, learning, and short-term memory and also has a general executive attention component. Impaired working memory is associated with a range of neurological and psychiatric disorders, but very little is known about how working memory relates to underlying white matter (WM) microstructure. In this study, we investigate the association between WM microstructure and performance on working memory tasks in healthy adults (right-handed, native English speakers). We combine compartment specific WM tract integrity (WMTI) metrics derived from multi-shell diffusion MRI as well as diffusion tensor/kurtosis imaging (DTI/DKI) metrics with Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests tapping auditory working memory. WMTI is a novel tool that helps us describe the microstructural characteristics in both the intra- and extra-axonal environments of WM such as axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities, allowing a more biophysical interpretation of WM changes. We demonstrate significant positive correlations between AWF and letter-number sequencing (LNS), suggesting that higher AWF with better performance on complex, more demanding auditory working memory tasks goes along with greater axonal volume and greater myelination in specific regions, causing efficient and faster information process.

Genetic relatedness of axial and radial diffusivity indices of cerebral white matter microstructure in late middle age

Two basic neuroimaging-based characterizations of white matter tracts are the magnitude of water diffusion along the principal tract orientation (axial diffusivity, AD) and water diffusion perpendicular to the principal orientation (radial diffusivity, RD). It is generally accepted that decreases in AD reflect disorganization, damage, or loss of axons, whereas increases in RD are indicative of disruptions to the myelin sheath. Previous reports have detailed the heritability of individual AD and RD measures, but have not examined the extent to which the same or different genetic or environmental factors influence these two phenotypes (except for corpus callosum). We implemented bivariate twin analyses to examine the shared and independent genetic influences on AD and RD. In the Vietnam Era Twin Study of Aging, 393 men (mean age = 61.8 years, SD = 2.6) underwent diffusion-weighted magnetic resonance imaging. We derived fractional anisotropy (FA), mean diffusivity (MD), AD, and RD estimates for 11 major bilateral white matter tracts and the mid-hemispheric corpus callosum, forceps major, and forceps minor. Separately, AD and RD were each highly heritable. In about three-quarters of the tracts, genetic correlations between AD and RD were >.50 (median = .67) and showed both unique and common variance. Genetic variance of FA and MD were predominately explained by RD over AD. These findings are important for informing genetic association studies of axonal coherence/damage and myelination/demyelination. Thus, genetic studies would benefit from examining the shared and unique contributions of AD and RD.

Diffusion Properties and 3D Architecture of Human Lower Leg Muscles Assessed with Ultra-High-Field-Strength Diffusion-Tensor MR Imaging and Tractography: Reproducibility and Sensitivity to Sex Difference and Intramuscular Variability

Purpose To demonstrate the reproducibility of the diffusion properties and three-dimensional structural organization measurements of the lower leg muscles by using diffusion-tensor imaging (DTI) assessed with ultra-high-field-strength (7.0-T) magnetic resonance (MR) imaging and tractography of skeletal muscle fibers. On the basis of robust statistical mapping analyses, this study also aimed at determining the sensitivity of the measurements to sex difference and intramuscular variability. Materials and Methods All examinations were performed with ethical review board approval; written informed consent was obtained from all volunteers. Reproducibility of diffusion tensor indexes assessment including eigenvalues, mean diffusivity, and fractional anisotropy (FA) as well as muscle volume and architecture (ie, fiber length and pennation angle) were characterized in lower leg muscles (n = 8). Intramuscular variability and sex differences were characterized in young healthy men and women (n = 10 in each group). Student t test, statistical parametric mapping, correlation coefficients (Spearman rho and Pearson product-moment) and coefficient of variation (CV) were used for statistical data analysis. Results High reproducibility of measurements (mean CV ± standard deviation, 4.6% ± 3.8) was determined in diffusion properties and architectural parameters. Significant sex differences were detected in FA (4.2% in women for the entire lower leg; P = .001) and muscle volume (21.7% in men for the entire lower leg; P = .008), whereas architecture parameters were almost identical across sex. Additional differences were found independently of sex in diffusion properties and architecture along several muscles of the lower leg. Conclusion The high-spatial-resolution DTI assessed with 7.0-T MR imaging allows a reproducible assessment of structural organization of superficial and deep muscles, giving indirect information on muscle function. ^©RSNA, 2018 Online supplemental material is available for this article.

Voxel Level Radiologic-Pathologic Validation of Restriction Spectrum Imaging Cellularity Index with Gleason Grade in Prostate Cancer

PURPOSE

Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions.

EXPERIMENTAL DESIGN

Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented as normalized z-score maps. In total, 2,795 tiles were analyzed and compared with RSI-MRI cellularity.

RESULTS

RSI-MRI cellularity index was found to distinguish between prostate cancer and benign tumor (t = 25.48, P < 0.00001). Significant differences were also found between benign tissue and prostate cancer classified as low-grade (GG = 3; t = 11.56, P < 0.001) or high-grade (GG ≥ 4; t = 24.03, P < 0.001). Furthermore, RSI-MRI differentiated between low and high-grade prostate cancer (t = 3.23; P = 0.003).

CONCLUSIONS

Building on our previous findings of correlation between GG and the RSI-MRI among whole tumors, our current study reveals a similar correlation at voxel resolution within tumors. Because it can detect variations in tumor grade with voxel-level precision, RSI-MRI may become an option for planning targeted procedures where identifying the area with the most aggressive disease is important. Clin Cancer Res; 22(11); 2668-74. ©2016 AACR.

Distortion correction of echo planar images applying the concept of finite rate of innovation to point spread function mapping (FRIP)

OBJECTIVE

Point spread function (PSF) mapping enables estimating the displacement fields required for distortion correction of echo planar images. Recently, a highly accelerated approach was introduced for estimating displacements from the phase slope of under-sampled PSF mapping data. Sampling schemes with varying spacing were proposed requiring stepwise phase unwrapping. To avoid unwrapping errors, an alternative approach applying the concept of finite rate of innovation to PSF mapping (FRIP) is introduced, using a pattern search strategy to locate the PSF peak, and the two methods are compared.

MATERIALS AND METHODS

Fully sampled PSF data was acquired in six subjects at 3.0 T, and distortion maps were estimated after retrospective under-sampling. The two methods were compared for both previously published and newly optimized sampling patterns. Prospectively under-sampled data were also acquired. Shift maps were estimated and deviations relative to the fully sampled reference map were calculated.

RESULTS

The best performance was achieved when using FRIP with a previously proposed sampling scheme. The two methods were comparable for the remaining schemes. The displacement field errors tended to be lower as the number of samples or their spacing increased.

CONCLUSION

A robust method for estimating the position of the PSF peak has been introduced.

Distortion Correction in Fetal EPI Using Non-Rigid Registration With a Laplacian Constraint

Geometric distortion induced by the main B0 field disrupts the consistency of fetal echo planar imaging (EPI) data, on which diffusion and functional magnetic resonance imaging is based. In this paper, we present a novel data-driven method for simultaneous motion and distortion correction of fetal EPI. A motion-corrected and reconstructed T2 weighted single shot fast spin echo (ssFSE) volume is used as a model of undistorted fetal brain anatomy. Our algorithm interleaves two registration steps: estimation of fetal motion parameters by aligning EPI slices to the model; and deformable registration of EPI slices to slices simulated from the undistorted model to estimate the distortion field. The deformable registration is regularized by a physically inspired Laplacian constraint, to model distortion induced by a source-free background B0 field. Our experiments show that distortion correction significantly improves consistency of reconstructed EPI volumes with ssFSE volumes. In addition, the estimated distortion fields are consistent with fields calculated from acquired field maps, and the Laplacian constraint is essential for estimation of plausible distortion fields. The EPI volumes reconstructed from different scans of the same subject were more consistent when the proposed method was used in comparison with EPI volumes reconstructed from data distortion corrected using a separately acquired B0 field map.

Decreased neurite density within frontostriatal networks is associated with executive dysfunction in temporal lobe epilepsy

OBJECTIVE

Executive dysfunction is observed in a sizable number of patients with refractory temporal lobe epilepsy (TLE). The frontostriatal network has been proposed to play a significant role in executive functioning, however, because of the complex architecture of these tracts, it is difficult to generate measures of fiber tract microstructure using standard diffusion tensor imaging. To examine the association between frontostriatal network compromise and executive dysfunction in TLE, we applied an advanced, multishell diffusion model, restriction spectrum imaging (RSI), that isolates measures of intraaxonal diffusion and may provide better estimates of fiber tract compromise in TLE.

METHODS

Restriction spectrum imaging scans were obtained from 32 patients with TLE [16 right TLE (RTLE); 16 left TLE (LTLE)] and 24 healthy controls (HC). An RSI-derived measure of intraaxonal anisotropic diffusion (neurite density; ND) was calculated for the inferior frontostriatal tract (IFS) and superior frontostriatal tract (SFS) and compared between patients with TLE and HC. Spearman correlations were performed to evaluate the relationships between ND of each tract and verbal (i.e., D-KEFS Category Switching Accuracy and Color-Word Interference Inhibition/Switching) and visuomotor (Trail Making Test) set-shifting performances in patients with TLE.

RESULTS

Patients with TLE demonstrated reductions in ND of the left and right IFS, but not SFS, compared with HC. Reduction in ND of left and right IFS was associated with poorer performance on verbal set-shifting in TLE. Increases in extracellular diffusion (isotropic hindered; IH) were not associated with executive dysfunction in the patient group.

SIGNIFICANCE

Restriction spectrum imaging-derived ND revealed microstructural changes within the IFS in patients with TLE, which was associated with poorer executive functioning. This suggests that axonal/myelin loss to fiber networks connecting the striatum to the inferior frontal cortex is likely contributing to executive dysfunction in TLE.

Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data

Because of their low bandwidth in the phase-encode (PE) direction, the susceptibility-induced off-resonance field causes distortions in echo planar imaging (EPI) images. It is therefore crucial to correct for susceptibility-induced distortions when performing diffusion studies using EPI. The susceptibility-induced field is caused by the object (head) disrupting the field and it is typically assumed that it remains constant within a framework defined by the object, (i.e. it follows the object as it moves in the scanner).

However, this is only approximately true. When a non-spherical object rotates around an axis other than that parallel with the magnetic flux (the z-axis) it changes the way it disrupts the field, leading to different distortions. Hence, if using a single field to correct for distortions there will be residual distortions in the volumes where the object orientation is substantially different to that when the field was measured.

In this paper we present a post-processing method for estimating the field as it changes with motion during the course of an experiment. It only requires a single measured field and knowledge of the orientation of the subject when that field was acquired. The volume-to-volume changes of the field as a consequence of subject movement are estimated directly from the diffusion data without the need for any additional or special acquisitions. It uses a generative model that predicts how each volume would look predicated on field change and inverts that model to yield an estimate of the field changes. It has been validated on both simulations and experimental data. The results show that we are able to track the field with high accuracy and that we are able to correct the data for the adverse effects of the changing field.

Identifying Rodent Resting-State Brain Networks with Independent Component Analysis

Rodent models have opened the door to a better understanding of the neurobiology of brain disorders and increased our ability to evaluate novel treatments. Resting-state functional magnetic resonance imaging (rs-fMRI) allows for in vivo exploration of large-scale brain networks with high spatial resolution. Its application in rodents affords researchers a powerful translational tool to directly assess/explore the effects of various pharmacological, lesion, and/or disease states on known neural circuits within highly controlled settings. Integration of animal and human research at the molecular-, systems-, and behavioral-levels using diverse neuroimaging techniques empowers more robust interrogations of abnormal/ pathological processes, critical for evolving our understanding of neuroscience. We present a comprehensive protocol to evaluate resting-state brain networks using Independent Component Analysis (ICA) in rodent model. Specifically, we begin with a brief review of the physiological basis for rs-fMRI technique and overview of rs-fMRI studies in rodents to date, following which we provide a robust step-by-step approach for rs-fMRI investigation including data collection, computational preprocessing, and brain network analysis. Pipelines are interwoven with underlying theory behind each step and summarized methodological considerations, such as alternative methods available and current consensus in the literature for optimal results. The presented protocol is designed in such a way that investigators without previous knowledge in the field can implement the analysis and obtain viable results that reliably detect significant differences in functional connectivity between experimental groups. Our goal is to empower researchers to implement rs-fMRI in their respective fields by incorporating technical considerations to date into a workable methodological framework.

Task-evoked pupil dilation and BOLD variance as indicators of locus coeruleus dysfunction

Pupillary responses during cognitive tasks are linked to functioning of the locus coeruleus (LC). The LC is an early site of abnormal tau deposition, which may contribute to key aspects of Alzheimer's disease (AD) pathophysiology. We previously found attenuation of pupillary responses to increases in cognitive load in individuals with mild cognitive impairment (MCI), suggesting pupillary responses may provide a biomarker of early risk for AD associated with LC dysfunction. The LC modulates cortical activity through two modes of operation: tonic and phasic. Early LC damage has been predicted to result in a state of persistent high tonic LC activity that may disrupt task-related phasic activity. To further examine whether pupillary responses are associated with early LC dysfunction, we measured pupil dilation during a digit span task as a measure of phasic activity, and low frequency BOLD variance (LFBV) during resting-state fMRI in key nodes of the ventral attention network (VAN) as a measure of cortical reactivity related to LC tonic activity in 358 middle-aged men. Individuals with greater LFBV in VAN nodes, i.e., higher tonic brain activity at rest, showed a smaller increase in pupil dilation from low to moderate cognitive loads. Thus, higher tonic LFBV activity at rest was related to reduced task-appropriate phasic dilation increases. The results support predictions from prominent models of LC functioning in which early LC dysfunction leads to persistent high tonic rates of activity during rest and lower signal-to-noise of phasic responses during task performance. Taken together with previous findings of early AD pathophysiology in LC and reduced phasic dilation responses to increased cognitive load in individuals with MCI, the present results suggest that pupillary responses may index early LC dysfunction and should receive further study as a potential biomarker of risk for AD.

Effects of Field-Map Distortion Correction on Resting State Functional Connectivity MRI

Magnetic field inhomogeneities cause geometric distortions of echo planar images used for functional magnetic resonance imaging (fMRI). To reduce this problem, distortion correction (DC) with field map is widely used for both task and resting-state fMRI (rs-fMRI). Although DC with field map has been reported to improve the quality of task fMRI, little is known about its effects on rs-fMRI. Here, we tested the influence of field-map DC on rs-fMRI results using two rs-fMRI datasets derived from 40 healthy subjects: one with DC (DC+) and the other without correction (DC−). Independent component analysis followed by the dual regression approach was used for evaluation of resting-state functional connectivity networks (RSN). We also obtained the ratio of low-frequency to high-frequency signal power (0.01–0.1 Hz and above 0.1 Hz, respectively; LFHF ratio) to assess the quality of rs-fMRI signals. For comparison of RSN between DC+ and DC− datasets, the default mode network showed more robust functional connectivity in the DC+ dataset than the DC− dataset. Basal ganglia RSN showed some decreases in functional connectivity primarily in white matter, indicating imperfect registration/normalization without DC. Supplementary seed-based and simulation analyses supported the utility of DC. Furthermore, we found a higher LFHF ratio after field map correction in the anterior cingulate cortex, posterior cingulate cortex, ventral striatum, and cerebellum. In conclusion, field map DC improved detection of functional connectivity derived from low-frequency rs-fMRI signals. We encourage researchers to include a DC step in the preprocessing pipeline of rs-fMRI analysis.

Preterm birth leads to hyper-reactive cognitive control processing and poor white matter organization in adulthood

Individuals born preterm with very low birth weight (VLBW; birth weight ≤ 1500 g) are at high risk for perinatal brain injuries and deviant brain development, leading to increased chances of later cognitive, emotional, and behavioral problems. Here we investigated the neuronal underpinnings of both reactive and proactive cognitive control processes in adults with VLBW. We included 32 adults born preterm with VLBW (before 37th week of gestation) and 32 term-born controls (birth weight ≥10th percentile for gestational age) between 22 and 24 years of age that have been followed prospectively since birth. Participants performed a well-validated Not-X continuous performance test (CPT) adapted for use in a mixed block- and event-related fMRI protocol. BOLD fMRI and DTI data was acquired on a 3T scanner. Performance on the Not-X CPT was highly similar between groups. However, the VLBW group demonstrated hyper-reactive cognitive control processing and disrupted white matter organization. The hyper-reactive brain activation signature in VLBW adults was associated with lower gestational age, lower fluid intelligence score, and anxiety problems. Automated Multi-Atlas Tract Extraction (AutoMATE) analyses revealed that this disruption of normal brain function was accompanied by poorer white matter organization in the anterior thalamic radiation and the cingulum, as reflected in both reduced fractional anisotropy and increased mean diffusivity. These findings show that the preterm behavioral phenotype is associated with predominantly reactive-, rather than proactive cognitive control processing, as well as white matter abnormalities, that may underlie common difficulties that many preterm born individuals experience in everyday life.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study

BACKGROUND

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning.

PURPOSE

To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM).

STUDY TYPE

Prospective.

SUBJECTS

Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+).

FIELD STRENGTH/SEQUENCE

Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values.

ASSESSMENT

Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping.

STATISTICAL TESTS

Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons.

RESULTS

For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity.

DATA CONCLUSION

Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216.

A method for mapping and quantifying whole organ diffusion-weighted image distortion in MR imaging of the prostate

A computational algorithm was designed to produce a measure of DW image distortion across the prostate. This algorithm was tested and validated on virtual phantoms incorporating known degrees and distributions of distortion. A study was then carried out on DW image volumes from three sets of 10 patients who had been imaged previously. These volumes had been radiologically assessed to have, respectively, 'no distortion' or 'significant distortion' or the potential for 'significant distortion' due to susceptibility effects from hip prostheses. Prostate outlines were drawn on a T2-weighted (T2W) image 'gold-standard' volume and on an ADC image volume derived from DW images acquired over the same region. The algorithm was then applied to these outlines to quantify and map image distortion. The proposed method correctly reproduced known distortion values and distributions in virtual phantoms. It also successfully distinguished between the three groups of patients: mean distortion in 'non-distorted' image volumes, 1.942 ± 0.582 mm; 'distorted', 4.402 ± 1.098 mm; and 'hip patients' 8.083 ± 4.653 mm; P < 0.001. This work has demonstrated and validated a means of quantifying and mapping image distortion in clinical prostate MRI cases.

White matter alterations and their associations with motor function in young adults born preterm with very low birth weight

Very low birth weight (VLBW: ≤ 1500 g) individuals have an increased risk of white matter alterations and neurodevelopmental problems, including fine and gross motor problems. In this hospital-based follow-up study, the main aim was to examine white matter microstructure and its relationship to fine and gross motor function in 31 VLBW young adults without cerebral palsy compared with 31 term-born controls, at mean age 22.6 ± 0.7 years. The participants were examined with tests of fine and gross motor function (Trail Making Test-5: TMT-5, Grooved Pegboard, Triangle from Movement Assessment Battery for Children-2: MABC-2 and High-level Mobility Assessment Tool: HiMAT) and diffusion tensor imaging (DTI). Probabilistic tractography of motor pathways of the corticospinal tract (CST) and corpus callosum (CC) was performed. Fractional anisotropy (FA) was calculated in non-crossing (capsula interna in CST, body of CC) and crossing (centrum semiovale) fibre regions along the tracts and examined for group differences. Associations between motor test scores and FA in the CST and CC were investigated with linear regression. Tract-based spatial statistics (TBSS) was used to examine group differences in DTI metrics in all major white matter tracts. The VLBW group had lower scores on all motor tests compared with controls, however, only statistically significant for TMT-5. Based on tractography, FA in the VLBW group was lower in non-crossing fibre regions and higher in crossing fibre regions of the CST compared with controls. Within the VLBW group, poorer fine motor function was associated with higher FA in crossing fibre regions of the CST, and poorer bimanual coordination was additionally associated with lower FA in crossing fibre regions of the CC. Poorer gross motor function was associated with lower FA in crossing fibre regions of the CST and CC. There were no associations between motor function and FA in non-crossing fibre regions of the CST and CC within the VLBW group. In the TBSS analysis, the VLBW group had lower FA and higher mean diffusivity compared with controls in all major white matter tracts. The findings in this study may indicate that the associations between motor function and FA are caused by other tracts crossing the CST and CC, and/or by alterations in the periventricular white matter in the centrum semiovale. Some of the associations were in the opposite direction than hypothesized, thus higher FA does not always indicate better function. Furthermore, widespread white matter alterations in VLBW individuals persist into young adulthood.

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Motor function was associated with FA in crossing fibre regions of CST and CC in VLBW young adults

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In crossing fibre regions of CST, FA was higher in VLBW than in control young adults

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TBSS showed lower FA and higher MD in white matter tracts in VLBW than in control young adults

Connectome imaging for mapping human brain pathways

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research.

Geometric distortion correction in prostate diffusion-weighted MRI and its effect on quantitative apparent diffusion coefficient analysis

PURPOSE

To evaluate the effect of correction for B₀ inhomogeneity-induced geometric distortion in echo-planar diffusion-weighted imaging on quantitative apparent diffusion coefficient (ADC) analysis in multiparametric prostate MRI.

METHODS

Geometric distortion correction was performed in echo-planar diffusion-weighted images (b = 0, 50, 400, 800 s/mm² ) of 28 patients, using two b₀ scans with opposing phase-encoding polarities. Histology-matched tumor and healthy tissue volumes of interest delineated on T₂ -weighted images were mapped to the nondistortion-corrected and distortion-corrected data sets by resampling with and without spatial coregistration. The ADC values were calculated on the volume and voxel level. The effect of distortion correction on ADC quantification and tissue classification was evaluated using linear-mixed models and logistic regression, respectively.

RESULTS

Without coregistration, the absolute differences in tumor ADC (range: 0.0002-0.189 mm² /s×10^-3 (volume level); 0.014-0.493 mm² /s×10^-3 (voxel level)) between the nondistortion-corrected and distortion-corrected were significantly associated (P < 0.05) with distortion distance (mean: 1.4 ± 1.3 mm; range: 0.3-5.3 mm). No significant associations were found upon coregistration; however, in patients with high rectal gas residue, distortion correction resulted in improved spatial representation and significantly better classification of healthy versus tumor voxels (P < 0.05).

CONCLUSIONS

Geometric distortion correction in DWI could improve quantitative ADC analysis in multiparametric prostate MRI. Magn Reson Med 79:2524-2532, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Stability metrics for optic radiation tractography: Towards damage prediction after resective surgery

BACKGROUND

An accurate delineation of the optic radiation (OR) using diffusion MR tractography may reduce the risk of a visual field deficit after temporal lobe resection. However, tractography is prone to generate spurious streamlines, which deviate strongly from neighboring streamlines and hinder a reliable distance measurement between the temporal pole and the Meyer's loop (ML-TP distance).

NEW METHOD

Stability metrics are introduced for the automated removal of spurious streamlines near the Meyer's loop. Firstly, fiber-to-bundle coherence (FBC) measures can identify spurious streamlines by estimating their alignment with the surrounding streamline bundle. Secondly, robust threshold selection removes spurious streamlines while preventing an underestimation of the extent of the Meyer's loop. Standardized parameter selection is realized through test-retest evaluation of the variability in ML-TP distance.

RESULTS

The variability in ML-TP distance after parameter selection was below 2mm for each of the healthy volunteers studied (N=8). The importance of the stability metrics is illustrated for epilepsy surgery candidates (N=3) for whom the damage to the Meyer's loop was evaluated by comparing the pre- and post-operative OR reconstruction. The difference between predicted and observed damage is in the order of a few millimeters, which is the error in measured ML-TP distance.

COMPARISON WITH EXISTING METHOD(S)

The stability metrics are a novel method for the robust estimate of the ML-TP distance.

CONCLUSIONS

The stability metrics are a promising tool for clinical trial studies, in which the damage to the OR can be related to the visual field deficit that may occur after epilepsy surgery.

Sensitivity of restriction spectrum imaging to memory and neuropathology in Alzheimer's disease

Background

Diffusion imaging has demonstrated sensitivity to structural brain changes in Alzheimer’s disease (AD). However, there remains a need for a more complete characterization of microstructural alterations occurring at the earliest disease stages, and how these changes relate to underlying neuropathology. This study evaluated the sensitivity of restriction spectrum imaging (RSI), an advanced diffusion magnetic resonance imaging (MRI) technique, to microstructural brain changes in mild cognitive impairment (MCI) and AD.

Methods

MRI and neuropsychological test data were acquired from 31 healthy controls, 12 individuals with MCI, and 13 individuals with mild AD, aged 63–93 years. Cerebrospinal fluid amyloid-β levels were measured in a subset (n = 38) of participants. RSI measures of neurite density (ND) and isotropic free water (IF) were computed in fiber tracts and in hippocampal and entorhinal cortex gray matter, respectively. Analyses evaluated whether these measures predicted memory performance, correlated with amyloid-β levels, and distinguished impaired individuals from controls. For comparison, analyses were repeated with standard diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA) and mean diffusivity.

Results

Both RSI and DTI measures correlated with episodic memory and disease severity. RSI, but not DTI, measures correlated with amyloid-β42 levels. ND and FA in the arcuate fasciculus and entorhinal cortex IF most strongly predicted recall performance. RSI measures of arcuate fasciculus ND and entorhinal cortex IF best discriminated memory impaired participants from healthy participants.

Conclusions

RSI is highly sensitive to microstructural changes in the early stages of AD, and is associated with biochemical markers of AD pathology. Reduced ND in cortical association fibers and increased medial temporal lobe free-water diffusion predicted episodic memory, distinguished cognitively impaired from healthy individuals, and correlated with amyloid-β. Although further research is needed to assess the sensitivity of RSI to preclinical AD and disease progression, these results suggest that RSI may be a promising tool to better understand neuroanatomical changes in AD and their association with neuropathology.

Electronic supplementary material

The online version of this article (doi:10.1186/s13195-017-0281-7) contains supplementary material, which is available to authorized users.

The impact of T1 versus EPI spatial normalization templates for fMRI data analyses

Spatial normalization of brains to a standardized space is a widely used approach for group studies in functional magnetic resonance imaging (fMRI) data. Commonly used template‐based approaches are complicated by signal dropout and distortions in echo planar imaging (EPI) data. The most widely used software packages implement two common template‐based strategies: (1) affine transformation of the EPI data to an EPI template followed by nonlinear registration to an EPI template (EPInorm) and (2) affine transformation of the EPI data to the anatomic image for a given subject, followed by nonlinear registration of the anatomic data to an anatomic template, which produces a transformation that is applied to the EPI data (T1norm). EPI distortion correction can be used to adjust for geometric distortion of EPI relative to the T1 images. However, in practice, this EPI distortion correction step is often skipped. We compare these template‐based strategies empirically in four large datasets. We find that the EPInorm approach consistently shows reduced variability across subjects, especially in the case when distortion correction is not applied. EPInorm also shows lower estimates for coregistration distances among subjects (i.e., within‐dataset similarity is higher). Finally, the EPInorm approach shows higher T values in a task‐based dataset. Thus, the EPInorm approach appears to amplify the power of the sample compared to the T1norm approach when not using distortion correction (i.e., the EPInorm boosts the effective sample size by 12–25%). In sum, these results argue for the use of EPInorm over the T1norm when no distortion correction is used. Hum Brain Mapp 38:5331–5342, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Multimodal imaging of language reorganization in patients with left temporal lobe epilepsy

This study explored the relationships among multimodal imaging, clinical features, and language impairment in patients with left temporal lobe epilepsy (LTLE). Fourteen patients with LTLE and 26 controls underwent structural MRI, functional MRI, diffusion tensor imaging, and neuropsychological language tasks. Laterality indices were calculated for each imaging modality and a principal component (PC) was derived from language measures. Correlations were performed among imaging measures, as well as to the language PC. In controls, better language performance was associated with stronger left-lateralized temporo-parietal and temporo-occipital activations. In LTLE, better language performance was associated with stronger right-lateralized inferior frontal, temporo-parietal, and temporo-occipital activations. These right-lateralized activations in LTLE were associated with right-lateralized arcuate fasciculus fractional anisotropy. These data suggest that interhemispheric language reorganization in LTLE is associated with alterations to perisylvian white matter. These concurrent structural and functional shifts from left to right may help to mitigate language impairment in LTLE.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Correction of metal-induced susceptibility artifacts for functional MRI during deep brain stimulation

Functional magnetic resonance imaging (fMRI) is an emerging tool for investigating brain activation associated with, or modulated by, deep brain stimulation (DBS). However, DBS-fMRI generally suffers from severe susceptibility to artifacts in regions near the metallic stimulation electrodes, as well as near tissue/air boundaries of the brain. These result in strong intensity and geometric distortions along the phase-encoding (PE) (i.e., blipped) direction in gradient-echo echo-planar imaging (GE-EPI). Distortion presents a major challenge to conducting reliable data analysis and in interpreting the findings. A recent study showed that the point spread function (PSF) mapping-based reverse gradient approach has a potential to correct for distortions not only in spin-echo EPI, but also in GE-EPI acquired in both the forward and reverse PE directions. In this study, we adapted that approach in order to minimize severe metal-induced susceptibility artifacts for DBS-fMRI, and to evaluate the performance of the approach in a phantom study and a large animal DBS-fMRI study. The method combines the distortion-corrected GE-EPI pair with geometrically different intensity distortions due to the opposing encoding directions. The results demonstrate that the approach can minimize susceptibility artifacts that appear around the metallic electrodes, as well as in the regions near the tissue/air boundaries in the brain. We also demonstrated that an accurate geometric correction is important in improving BOLD contrast in the group dataset, especially in regions where strong susceptibility artifacts appear.

Regional susceptibility to dose-dependent white matter damage after brain radiotherapy

BACKGROUND AND PURPOSE

Regional differences in sensitivity to white matter damage after brain radiotherapy (RT) are not well-described. We characterized the spatial heterogeneity of dose-response across white matter tracts using diffusion tensor imaging (DTI).

MATERIALS AND METHODS

Forty-nine patients with primary brain tumors underwent MRI with DTI before and 9-12months after partial-brain RT. Maps of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were generated. Atlas-based white matter tracts were identified. A secondary analysis using skeletonized tracts was also performed. Linear mixed-model analysis of the relationship between mean and max dose and percent change in DTI metrics was performed.

RESULTS

Tracts with the strongest correlation of FA change with mean dose were the fornix (-0.46 percent/Gy), cingulum bundle (-0.44 percent/Gy), and body of corpus callosum (-0.23 percent/Gy), p<.001. These tracts also showed dose-sensitive changes in MD and RD. In the skeletonized analysis, the fornix and cingulum bundle remained highly dose-sensitive. Maximum and mean dose were similarly predictive of DTI change.

CONCLUSIONS

The corpus callosum, cingulum bundle, and fornix show the most prominent dose-dependent changes following RT. Future studies examining correlation with cognitive functioning and potential avoidance of critical white matter regions are warranted.

Block-Matching Distortion Correction of Echo-Planar Images With Opposite Phase Encoding Directions

By shortening the acquisition time of MRI, Echo Planar Imaging (EPI) enables the acquisition of a large number of images in a short time, compatible with clinical constraints as required for diffusion or functional MRI. However such images are subject to large, local distortions disrupting their correspondence with the underlying anatomy. The correction of those distortions is an open problem, especially in regions where large deformations occur. We propose a new block-matching registration method to perform EPI distortion correction based on the acquisition of two EPI with opposite phase encoding directions (PED). It relies on new transformations between blocks adapted to the EPI distortion model, and on an adapted optimization scheme to ensure an opposite symmetric transformation. We present qualitative and quantitative results of the block-matching correction using different metrics on a phantom dataset and on in-vivo data. We show the ability of the block-matching to robustly correct EPI distortion even in strongly affected areas.

Evaluating the effect of rectal distension on prostate multiparametric MRI image quality

PURPOSE

To evaluate the effect of rectal distension on the quality of anatomical and functional prostate multiparametric (mp) MRI.

MATERIALS AND METHODS

Multiparametric (mp) 3T-MRI images of 173 patients were independently evaluated by two radiologists in this retrospective study. Planimetry rectal volumes were derived and a subjective assessment of rectal distension was made using a 5-point Likert scale (1=no stool/gas, 5=large amount of stool/gas). Image quality of diffusion-weighted imaging (DWI) was evaluated using a 5-point Likert scale. DWI was further scored for distortion and artefact. T2W images were evaluated for image sharpness and the presence of motion artefact. The stability of the dynamic contrast-enhancement acquisition was assessed by recording the number of corrupt data points during the wash-out phase.

RESULTS

There was a strong correlation between subjective scoring of rectal loading and objectively measured rectal volume (r=0.82), p<0.001. A significant correlation was shown between increased rectal distension and both reduced DW image quality (r=-0.628, p<0.001), and increased DW image distortion (r=0.814, p<0.001). There was also a significant trend for rectal distension to increase artefact at DWI (r=0.154, p=0.042). Increased rectal distension led to increased motion artefact on T2 (p=0.0096), but did not have a significant effect on T2-sharpness (p=0.0638). There was no relationship between rectal distension and DCE image quality (p=0.693). 63 patients underwent lesion-targeted biopsy post MRI, there was a trend to higher positive predictive values in patients with minor rectal distension (34/38, 89.5%) compared to those with moderate/marked distension (18/25, 72%), p=0.09.

CONCLUSION

Rectal distension has a significant negative effect on the quality of both T2W and DW images. Consideration should therefore be given to bowel preparation prior to prostate mpMRI to optimise image quality.

Restriction Spectrum Imaging Improves Risk Stratification in Patients with Glioblastoma

BACKGROUND AND PURPOSE

ADC as a marker of tumor cellularity has been promising for evaluating the response to therapy in patients with glioblastoma but does not successfully stratify patients according to outcomes, especially in the upfront setting. Here we investigate whether restriction spectrum imaging, an advanced diffusion imaging model, performed after an operation but before radiation therapy, could improve risk stratification in patients with newly diagnosed glioblastoma relative to ADC.

MATERIALS AND METHODS

Pre-radiation therapy diffusion-weighted and structural imaging of 40 patients with glioblastoma were examined retrospectively. Restriction spectrum imaging and ADC-based hypercellularity volume fraction (restriction spectrum imaging-FLAIR volume fraction, restriction spectrum imaging-contrast-enhanced volume fraction, ADC-FLAIR volume fraction, ADC-contrast-enhanced volume fraction) and intensities (restriction spectrum imaging-FLAIR 90th percentile, restriction spectrum imaging-contrast-enhanced 90th percentile, ADC-FLAIR 10th percentile, ADC-contrast-enhanced 10th percentile) within the contrast-enhanced and FLAIR hyperintensity VOIs were calculated. The association of diffusion imaging metrics, contrast-enhanced volume, and FLAIR hyperintensity volume with progression-free survival and overall survival was evaluated by using Cox proportional hazards models.

RESULTS

Among the diffusion metrics, restriction spectrum imaging-FLAIR volume fraction was the strongest prognostic metric of progression-free survival (P = .036) and overall survival (P = .007) in a multivariate Cox proportional hazards analysis, with higher values indicating earlier progression and shorter survival. Restriction spectrum imaging-FLAIR 90th percentile was also associated with overall survival (P = .043), with higher intensities, indicating shorter survival. None of the ADC metrics were associated with progression-free survival/overall survival. Contrast-enhanced volume exhibited a trend toward significance for overall survival (P = .063).

CONCLUSIONS

Restriction spectrum imaging-derived cellularity in FLAIR hyperintensity regions may be a more robust prognostic marker than ADC and conventional imaging for early progression and poorer survival in patients with glioblastoma. However, future studies with larger samples are needed to explore its predictive ability.

Multi-component diffusion characterization of radiation-induced white matter damage

PURPOSE

We used multi-b-value diffusion models to characterize microstructural white matter changes after brain radiation into fast and slow components, in order to better understand the pathophysiology of radiation-induced tissue damage.

METHODS

Fourteen patients were included in this retrospective analysis with imaging prior to, and at 1, 4-5, and 9-10 months after radiotherapy (RT). Diffusion signal decay within brain white matter was fit to a biexponential model to separate changes within the slow and fast components. Linear mixed-effects models were used to obtain estimates of the effect of radiation dose and time on the model parameters.

RESULTS

We found an increase of 0.11 × 10^-4 and 0.14 × 10^-4 mm² /s in the fast diffusion coefficient per unit dose-time (Gy-month) in the longitudinal and transverse directions, respectively. By contrast, the longitudinal slow diffusion coefficient decreased independently of dose, by 0.18 × 10^-4 , 0.16 × 10^-4 , and 0.098 × 10^-4 mm² /s at 1, 4, and 9 months post-RT, respectively.

CONCLUSIONS

Radiation-induced white matter changes in the first year following RT are driven by dose-dependent increases in the fast component and dose-independent decreases in the slow component.

Cognitive phenotypes in parkinson's disease differ in terms of brain-network organization and connectivity

Cognitive deficits are common in Parkinson's disease and we suspect that dysfunctions of connected brain regions can be the source of these deficits. The aim of the present study was to investigate changes in whole-brain intrinsic functional connectivity according to differences in cognitive profiles in Parkinson's disease. 119 participants were enrolled and divided into four groups according to their cognitive phenotypes (determined by a cluster analysis): (i) 31 cognitively intact patients (G1), (ii) 31 patients with only slight mental slowing (G2), (iii) 43 patients with mild to moderate deficits mainly in executive functions (G3), (iv) 14 patients with severe deficits in all cognitive domains (G4-5). Rs-fMRI whole-brain connectivity was examined by two complementary approaches: graph theory for studying network functional organization and network-based statistics (NBS) for exploring functional connectivity amongst brain regions. After adjustment for age, duration of formal education and center of acquisition, there were significant group differences for all functional organization indexes: functional organization decreased (G1 > G2 > G3 > G4-5) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, P < 0.01) mainly concerned the ventral prefrontal, parietal, temporal and occipital cortices as well as the basal ganglia. In Parkinson's disease, brain network organization is progressively disrupted as cognitive impairment worsens, with an increasing number of altered connections between brain regions. We observed reduced connectivity in highly associative areas, even in patients with only slight mental slowing. The association of slowed mental processing with loss of connectivity between highly associative areas could be an early marker of cognitive impairment in Parkinson's disease and may contribute to the detection of prodromal forms of Parkinson's disease dementia. Hum Brain Mapp 38:1604-1621, 2017. © 2016 Wiley Periodicals, Inc.

Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution

PURPOSE

Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation.

METHODS

A generalization of the multi-shell spherical deconvolution model using a Richardson-Lucy algorithm is applied to noisy data. The reconstructed coefficients are then used in the forward model to compute denoised diffusion-weighted images (DWIs). The proposed method operates in the diffusion space and thus is complementary to image-based denoising methods.

RESULTS

We demonstrate improved image quality on the DWIs themselves, maps of neurite orientation dispersion and density imaging, and diffusional kurtosis imaging (DKI), as well as reduced spurious peaks in deterministic tractography. For DKI in particular, we observe up to 50% error reduction and demonstrate high image quality using just 30 DWIs. This corresponds to greater than fourfold reduction in scan time if compared to the widely used 140-DWI acquisitions. We also confirm consistent performance in pathological data sets, namely in white matter lesions of a multiple sclerosis patient.

CONCLUSION

The proposed denoising technique termed generalized spherical deconvolution has the potential of significantly improving image quality in diffusion MRI. Magn Reson Med 78:2428-2438, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Genetic and environmental influences on mean diffusivity and volume in subcortical brain regions

Increased mean diffusivity (MD) is hypothesized to reflect tissue degeneration and may provide subtle indicators of neuropathology as well as age-related brain changes in the absence of volumetric differences. Our aim was to determine the degree to which genetic and environmental variation in subcortical MD is distinct from variation in subcortical volume. Data were derived from a sample of 387 male twins (83 MZ twin pairs, 55 DZ twin pairs, and 111 incomplete twin pairs) who were MRI scanned as part of the Vietnam Era Twin Study of Aging. Quantitative estimates of MD and volume for 7 subcortical regions were obtained: thalamus, caudate nucleus, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens. After adjusting for covariates, bivariate twin models were fitted to estimate the size and significance of phenotypic, genotypic, and environmental correlations between MD and volume at each subcortical region. With the exception of the amygdala, familial aggregation in MD was entirely explained by additive genetic factors across all subcortical regions with estimates ranging from 46 to 84%. Based on bivariate twin modeling, variation in subcortical MD appears to be both genetically and environmentally unrelated to individual differences in subcortical volume. Therefore, subcortical MD may be an alternative biomarker of brain morphology for complex traits worthy of future investigation. Hum Brain Mapp 38:2589-2598, 2017. © 2017 Wiley Periodicals, Inc.

The cortical connectivity of the periaqueductal gray and the conditioned response to the threat of breathlessness

Previously we observed differential activation in individual columns of the periaqueductal grey (PAG) during breathlessness and its conditioned anticipation (Faull et al., 2016b). Here, we have extended this work by determining how the individual columns of the PAG interact with higher cortical centres, both at rest and in the context of breathlessness threat. Activation was observed in ventrolateral PAG (vlPAG) and lateral PAG (lPAG), where activity scaled with breathlessness intensity ratings, revealing a potential interface between sensation and cognition during breathlessness. At rest the lPAG was functionally correlated with cortical sensorimotor areas, conducive to facilitating fight/flight responses, and demonstrated increased synchronicity with the amygdala during breathlessness. The vlPAG showed fronto-limbic correlations at rest, whereas during breathlessness anticipation, reduced functional synchronicity was seen to both lPAG and motor structures, conducive to freezing behaviours. These results move us towards understanding how the PAG might be intricately involved in human responses to threat.

Genetic and environmental influences on cortical mean diffusivity

Magnetic resonance imaging (MRI) has become an important tool in the early detection of age-related and neuropathological brain changes. Recent studies suggest that changes in mean diffusivity (MD) of cortical gray matter derived from diffusion MRI scans may be useful in detecting early effects of Alzheimer's disease (AD), and that these changes may be detected earlier than alterations associated with standard structural MRI measures such as cortical thickness. Thus, due to its potential clinical relevance, we examined the genetic and environmental influences on cortical MD in middle-aged men to provide support for the biological relevance of this measure and to guide future gene association studies. It is not clear whether individual differences in cortical MD reflect neuroanatomical variability similarly detected by other MRI measures, or whether unique features are captured. For instance, variability in cortical MD may reflect morphological variability more commonly measured by cortical thickness. Differences among individuals in cortical MD may also arise from breakdowns in myelinated fibers running through the cortical mantle. Thus, we investigated whether genetic influences on variation in cortical MD are the same or different from those influencing cortical thickness and MD of white matter (WM) subjacent to the cortical ribbon. Univariate twin analyses indicated that cortical MD is heritable in the majority of brain regions; the average of regional heritability estimates ranged from 0.38 in the cingulate cortex to 0.66 in the occipital cortex, consistent with the heritability of other MRI measures of the brain. Trivariate analyses found that, while there was some shared genetic variance between cortical MD and each of the other two measures, this overlap was not complete (i.e., the correlation was statistically different from 1). A significant amount of distinct genetic variance influences inter-individual variability in cortical MD; therefore, this measure could be useful for further investigation in studies of neurodegenerative diseases and gene association studies.

Effects of Orientation and Anisometry of Magnetic Resonance Imaging Acquisitions on Diffusion Tensor Imaging and Structural Connectomes

Diffusion-weighted imaging (DWI) quantifies water molecule diffusion within tissues and is becoming an increasingly used technique. However, it is very challenging as correct quantification depends on many different factors, ranging from acquisition parameters to a long pipeline of image processing. In this work, we investigated the influence of voxel geometry on diffusion analysis, comparing different acquisition orientations as well as isometric and anisometric voxels. Diffusion-weighted images of one rat brain were acquired with four different voxel geometries (one isometric and three anisometric in different directions) and three different encoding orientations (coronal, axial and sagittal). Diffusion tensor scalar measurements, tractography and the brain structural connectome were analyzed for each of the 12 acquisitions. The acquisition direction with respect to the main magnetic field orientation affected the diffusion results. When the acquisition slice-encoding direction was not aligned with the main magnetic field, there were more artifacts and a lower signal-to-noise ratio that led to less anisotropic tensors (lower fractional anisotropic values), producing poorer quality results. The use of anisometric voxels generated statistically significant differences in the values of diffusion metrics in specific regions. It also elicited differences in tract reconstruction and in different graph metric values describing the brain networks. Our results highlight the importance of taking into account the geometric aspects of acquisitions, especially when comparing diffusion data acquired using different geometries.

Restriction Spectrum Imaging As a Potential Measure of Cortical Neurite Density in Autism

Autism postmortem studies have shown various cytoarchitectural anomalies in cortical and limbic areas including increased cell packing density, laminar disorganization, and narrowed minicolumns. However, there is little evidence on dendritic and axonal organization in ASD. Recent imaging techniques have the potential for non-invasive, in vivo studies of small-scale structure in the human brain, including gray matter. Here, Restriction Spectrum Imaging (RSI), a multi-shell diffusion-weighted imaging technique, was used to examine gray matter microstructure in 24 children with ASD (5 female) and 20 matched typically developing (TD) participants (2 female), ages 7–17 years. RSI extends the spherical deconvolution model to multiple length scales to characterize neurite density (ND) and organization. Measures were examined in 48 cortical regions of interest per hemisphere. To our knowledge, this is the first time that a multi-compartmental diffusion model has been applied to cortical gray matter in ASD. The ND measure detected robust age effects showing a significant positive relationship to age in all lobes except left temporal when groups were combined. Results were also suggestive of group differences (ASD<TD) in anterior cingulate, right superior temporal lobe and much of the parietal lobes, but these fell short of statistical significance. For MD, significant group differences (ASD>TD) in bilateral parietal regions as well as widespread age effects were detected. Our findings support the value of multi-shell diffusion imaging for assays of cortical gray matter. This approach has the potential to add to postmortem literature, examining intracortical organization, intracortical axonal content, myelination, or caliber. Robust age effects further support the validity of the ND metric for in vivo examination of gray matter microstructure in ASD and across development. While diffusion MRI does not approach the precision of histological studies, in vivo imaging measures of microstructure can complement postmortem studies, by allowing access to large sample sizes, a whole-brain field of view, longitudinal designs, and combination with behavioral and functional assays. This makes multi-shell diffusion imaging a promising technique for understanding the underlying cytoarchitecture of the disorder.

Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

Heritability of white matter microstructure in late middle age: A twin study of tract-based fractional anisotropy and absolute diffusivity indices

There is evidence that differences among individuals in white matter microstructure, as measured with diffusion tensor imaging (DTI), are under genetic control. However, little is known about the relative contribution of genetic and environmental effects on different diffusivity indices among late middle-aged adults. Here, we examined the magnitude of genetic influences for fractional anisotropy (FA), and mean (MD), axial (AD), and radial (RD) diffusivities in male twins aged 56-66 years old. Using an atlas-based registration approach to delineate individual white matter tracts, we investigated mean DTI-based indices within the corpus callosum, 12 bilateral tracts and all these regions of interest combined. All four diffusivity indices had high heritability at the global level (72%-80%). The magnitude of genetic effects in individual tracts varied from 0% to 82% for FA, 0% to 81% for MD, 8% to 77% for AD, and 0% to 80% for RD with most of the tracts showing significant heritability estimates. Despite the narrow age range of this community-based sample, age was correlated with all four diffusivity indices at the global level. In sum, all diffusion indices proved to have substantial heritability for most of the tracts and the heritability estimates were similar in magnitude for different diffusivity measures. Future studies could aim to discover the particular set of genes that underlie the significant heritability of white matter microstructure. Hum Brain Mapp 38:2026-2036, 2017. © 2017 Wiley Periodicals, Inc.

Dyslexia and language impairment associated genetic markers influence cortical thickness and white matter in typically developing children

Dyslexia and language impairment (LI) are complex traits with substantial genetic components. We recently completed an association scan of the DYX2 locus, where we observed associations of markers in DCDC2, KIAA0319, ACOT13, and FAM65B with reading-, language-, and IQ-related traits. Additionally, the effects of reading-associated DYX3 markers were recently characterized using structural neuroimaging techniques. Here, we assessed the neuroimaging implications of associated DYX2 and DYX3 markers, using cortical volume, cortical thickness, and fractional anisotropy. To accomplish this, we examined eight DYX2 and three DYX3 markers in 332 subjects in the Pediatrics Imaging Neurocognition Genetics study. Imaging-genetic associations were examined by multiple linear regression, testing for influence of genotype on neuroimaging. Markers in DYX2 genes KIAA0319 and FAM65B were associated with cortical thickness in the left orbitofrontal region and global fractional anisotropy, respectively. KIAA0319 and ACOT13 were suggestively associated with overall fractional anisotropy and left pars opercularis cortical thickness, respectively. DYX3 markers showed suggestive associations with cortical thickness and volume measures in temporal regions. Notably, we did not replicate association of DYX3 markers with hippocampal measures. In summary, we performed a neuroimaging follow-up of reading-, language-, and IQ-associated DYX2 and DYX3 markers. DYX2 associations with cortical thickness may reflect variations in their role in neuronal migration. Furthermore, our findings complement gene expression and imaging studies implicating DYX3 markers in temporal regions. These studies offer insight into where and how DYX2 and DYX3 risk variants may influence neuroimaging traits. Future studies should further connect the pathways to risk variants associated with neuroimaging/neurocognitive outcomes.

Assessing White Matter Microstructure in Brain Regions with Different Myelin Architecture Using MRI

OBJECTIVE

We investigate how known differences in myelin architecture between regions along the cortico-spinal tract and frontal white matter (WM) in 19 healthy adolescents are reflected in several quantitative MRI parameters that have been proposed to non-invasively probe WM microstructure. In a clinically feasible scan time, both conventional imaging sequences as well as microstructural MRI parameters were assessed in order to quantitatively characterise WM regions that are known to differ in the thickness of their myelin sheaths, and in the presence of crossing or parallel fibre organisation.

RESULTS

We found that diffusion imaging, MR spectroscopy (MRS), myelin water fraction (MWF), Magnetization Transfer Imaging, and Quantitative Susceptibility Mapping were myelin-sensitive in different ways, giving complementary information for characterising WM microstructure with different underlying fibre architecture. From the diffusion parameters, neurite density (NODDI) was found to be more sensitive than fractional anisotropy (FA), underlining the limitation of FA in WM crossing fibre regions. In terms of sensitivity to different myelin content, we found that MWF, the mean diffusivity and chemical-shift imaging based MRS yielded the best discrimination between areas.

CONCLUSION

Multimodal assessment of WM microstructure was possible within clinically feasible scan times using a broad combination of quantitative microstructural MRI sequences. By assessing new microstructural WM parameters we were able to provide normative data and discuss their interpretation in regions with different myelin architecture, as well as their possible application as biomarker for WM disorders.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.

Distortion inherent to magnetic resonance imaging can lead to geometric miss in radiosurgery planning

PURPOSE

Anatomic distortion is present in all magnetic resonance imaging (MRI) data because of nonlinearity of gradient fields; it measures up to several millimeters. We evaluated the potential for uncorrected MRI to lead to geometric miss of the target volume in stereotactic radiosurgery (SRS).

METHODS AND MATERIALS

Twenty-eight SRS cases were studied retrospectively. MRI scans were corrected for gradient nonlinearity distortion in 3 dimensions, and gross tumor volumes (GTVs) were contoured. The manufacturer-specified distortion field was then reapplied to GTV masks to allow measurement of GTV displacement in uncorrected images. The uncorrected GTV was used for SRS planning, and the dose received by the true (corrected) GTV was measured.

RESULTS

Median displacement of the GTV resulting from gradient distortion was 1.2 mm (interquartile range, 0.1-2.3 mm), with a minimum of 0 mm and a maximum of 3.9 mm. Eight of the 28 cases met a priori criteria for "geometric miss."

CONCLUSIONS

Although MRI distortion is often subtle on visual inspection, there is a significant clinical impact of this distortion on SRS planning. Distortion-corrected MRI should uniformly be used for intracranial radiosurgery planning because uncorrected MRI can lead to potential geometric miss.